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REVIEW 2 major objections 1 minor 78 references

Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution

T0 review · 2 major / 1 minor · reviewed 2026-06-27 · grok-4.3

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Pith's one-line read The sequence in which macros are placed during chip design is a decisive optimization factor that LLM evolution can automatically improve.

desk verdict OrderPlace's claimed gains rest on an unverified proxy whose ranking fidelity is not demonstrated, so the results stay provisional. read the letter →

arxiv 2606.08904 v1 pith:BSC455Z4 submitted 2026-06-08 cs.AI

Shibing Mo , Jing Liu , Jianchu Xu , Ruilin Wu This is my paper · ORCID
classification cs.AI
keywords macroplacementsequencingLLMevolutionproxyevaluationphysicaldesignwirelengthoptimizationcombinatorial
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper shows that placement order is not a minor preprocessing choice but a major driver of final solution quality, because early decisions create irreversible constraints on later placements. OrderPlace evolves new ordering policies with large language models, ranging from static metrics to dynamic mechanisms, and uses a fast deterministic greedy probe as a proxy to screen candidates before full evaluation. On the ISPD 2005 benchmarks this yields sequences that cut wirelength by 34.04 percent versus WireMask-EA and 14.08 percent versus EGPlace. A reader would care because the work treats sequencing as an optimizable dimension rather than a fixed heuristic, opening a route to better layouts without rewriting the core placer.

What carries the argument

Proxy-guided LLM evolution that generates and screens placement-ordering policies via a deterministic greedy probe used as a lightweight filter.

What would settle it

A sequence that the proxy ranks low but that produces lower wirelength than the proxy's top-ranked sequences when evaluated with the full placer would falsify the filtering claim.

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Extended reading notes

Core claim

OrderPlace demonstrates that placement sequences are a decisive factor in macro placement optimization; proxy-guided LLM evolution automatically discovers code-level ordering policies that produce lower wirelength than prior methods, with measured reductions of 34.04 percent relative to WireMask-EA and 14.08 percent relative to EGPlace on standard ISPD 2005 benchmarks.

Load-bearing premise

The greedy probe proxy ranks sequences accurately enough that the best full-evaluation performers are not discarded during filtering.

Editorial extensions

If this is right

  • Placement ordering can be treated as an explicit search dimension rather than a static heuristic based on area or connectivity.
  • Early sequencing choices create domino effects that permanently limit the reachable solution space.
  • Lightweight proxies make evolutionary discovery of ordering policies computationally practical.
  • Novel dynamic and physics-inspired ordering rules can be found automatically instead of being hand-crafted.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same proxy-guided evolution approach could be tested on sequencing decisions inside other stages of physical design such as routing or floorplanning.
  • Automatically discovered ordering policies might transfer across different benchmark suites or technology nodes without manual retuning.
  • Combining evolved ordering with existing ML coordinate predictors could produce compound gains in overall placement quality.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces OrderPlace, a proxy-guided LLM evolution framework for automatically discovering macro placement order strategies in chip physical design. It claims that placement sequencing is a decisive factor due to irreversible early decisions, explores code-level policies via LLM evolution, and uses a deterministic greedy probe as a lightweight proxy to filter candidates. On ISPD 2005 benchmarks, OrderPlace is reported to reduce wirelength by 34.04% compared to WireMask-EA and 14.08% compared to EGPlace, attributing the gains to novel discovered ordering strategies.

Significance. If the proxy mechanism is shown to produce rankings correlated with full placement evaluations, the work would be significant for electronic design automation by demonstrating that temporal ordering can be optimized beyond static heuristics and that LLM-based search can discover effective policies for high-dimensional combinatorial problems. The approach of evolving code-level mechanisms rather than fixed metrics offers a generalizable direction for heuristic discovery in VLSI optimization.

major comments (2)
  1. [Methods (proxy evaluation mechanism)] Methods section on proxy evaluation: the deterministic greedy probe proxy is presented without any reported quantitative validation (e.g., rank correlation, precision@K, or hold-out comparison against full wirelength evaluations on ISPD 2005 instances). This is load-bearing for the central claim because the evolutionary search filters all candidates exclusively via the proxy; any systematic mismatch would mean the reported novel strategies and wirelength gains are artifacts of the proxy rather than genuine improvements.
  2. [Experimental results] Experimental results section: the headline improvements (34.04% vs WireMask-EA, 14.08% vs EGPlace) are stated without error bars, number of independent runs, statistical significance tests, or details on how many sequences were evaluated under the full placer versus the proxy. This undermines assessment of whether the gains are robust or attributable to the discovered orderings.
minor comments (1)
  1. [Abstract] The abstract and methods do not specify the exact ISPD 2005 instances used or the full experimental protocol, which would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the proxy validation and experimental reporting. We address each major comment below and will revise the manuscript accordingly to strengthen the claims.

read point-by-point responses

  1. Referee: Methods section on proxy evaluation mechanism: the deterministic greedy probe proxy is presented without any reported quantitative validation (e.g., rank correlation, precision@K, or hold-out comparison against full wirelength evaluations on ISPD 2005 instances). This is load-bearing for the central claim because the evolutionary search filters all candidates exclusively via the proxy; any systematic mismatch would mean the reported novel strategies and wirelength gains are artifacts of the proxy rather than genuine improvements.

    Authors: We agree that explicit quantitative validation of the proxy is essential to support its use as a filter. In the revised manuscript, we will add a dedicated subsection reporting Spearman's rank correlation and Kendall's tau between proxy scores and full wirelength evaluations on all ISPD 2005 instances, along with precision@5 and precision@10 metrics and a hold-out comparison. These additions will directly demonstrate the proxy's ranking fidelity. revision: yes

  2. Referee: Experimental results section: the headline improvements (34.04% vs WireMask-EA, 14.08% vs EGPlace) are stated without error bars, number of independent runs, statistical significance tests, or details on how many sequences were evaluated under the full placer versus the proxy. This undermines assessment of whether the gains are robust or attributable to the discovered orderings.

    Authors: We acknowledge that the current experimental presentation lacks statistical rigor. In the revision, we will report results from 5 independent runs with standard deviations shown as error bars, include p-values from paired t-tests against the baselines, and explicitly state the number of sequences evaluated under the full placer (versus those filtered by proxy only). This will clarify the robustness of the reported wirelength gains. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical search results with independent proxy filter

full rationale

The paper presents an LLM evolutionary search over ordering policies, filtered by a deterministic greedy proxy before full placement evaluation. No equations, fitted parameters, or self-citations are described that would make the reported wirelength reductions equivalent to the inputs by construction. The proxy is an engineering choice whose correlation with true quality is an external assumption, not a definitional loop. The headline gains are benchmark outcomes, not renamings or self-referential predictions.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only review; no free parameters, axioms, or invented physical entities are stated. The OrderPlace framework itself is a new procedural method rather than a postulated entity with independent evidence.

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Cite this review

Pith. "Pith review of Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution." pith.science (2026). https://pith.science/paper/BSC455Z4

@misc{pith2026260608904,
  author       = {Pith},
  title        = {Pith review of: Order Matters: Unveiling the Hidden Impact of Macro Placement Sequences via Proxy-Guided LLM Evolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BSC455Z4}},
  note         = {Machine review of arXiv:2606.08904}
}
read the original abstract

Macro placement is a fundamental step in modern chip physical design, playing a crucial role in determining the solution quality of high-dimensional combinatorial optimization problems. Despite recent advancements in machine learning for spatial coordinate determination, the temporal dimension of placement sequencing remains largely governed by static heuristics. In this work, we demonstrate that the placement sequence is not merely a preprocessing step but a decisive factor in optimization, where suboptimal early decisions trigger irreversible domino effects that constrain the solution space. To harness this unexplored dimension, we propose \textbf{OrderPlace}, a proxy-guided LLM evolution framework for automatically discovering macro placement order strategies. Instead of relying on manually crafted heuristics such as area- or connectivity-based ordering, OrderPlace explores a broader space of code-level policies, ranging from static scoring metrics to dynamic physics-inspired mechanisms. To mitigate the prohibitive cost of evaluating sequences, we introduce a lightweight proxy evaluation mechanism that efficiently filters candidates using a deterministic greedy probe. Experimental results on the standard ISPD 2005 benchmarks demonstrate that OrderPlace discovers novel ordering strategies. Compared with WireMask-EA and the state-of-the-art method EGPlace, OrderPlace reduces wirelength by 34.04\% and 14.08\%, respectively.

Figures

Figures reproduced from arXiv: 2606.08904 by the authors.

Figure 1
Figure 1. Overview of OrderPlace. Part (a) illustrates the overall execution flow of OrderPlace, while part (b) presents the detailed procedure of the Population Quality Evaluator module. (a) Select Position: Determine the optimal position pt = (xt, yt) for the current macro πt that mini￾mizes the incremental wirelength cost: pt = argmin p∈Valid(Pt−1) ∆HPWL(πt, p, B) (1) (b) Update Placement: Pt = Pt−1 ∪ {(πt, pt)}. (c) Updat… view at source ↗
Figure 2
Figure 2. Comparison of HPWL Trend Over the Runtime(s). In these trajectories, OrderPlace(StrategyNumber) denotes the placement process utilizing the Number-th best ordering strategy identified by our framework. For each dataset, we select the top 4 strategies and perform parallel macro placement optimization, resulting in a total of 24 strategies. For specific strategy details, refer to Appendix F. • Select the position mini… view at source ↗
Figure 3
Figure 3. Experimental Analysis Results for Parameter Top-K [PITH_FULL_IMAGE:figures/full_fig_p032_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Below subfigure is the convergence curve of the best proxy HPWL as the generation number increases, where the bar chart represents the elite population update rate for each generation. Above subfigure is the composition of the elite population for each generation, with…

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    2025

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.